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Jun Yan

  1. Goodness-of-fit testing based on a weighted bootstrap: A fast large-sample alternative to the parametric bootstrap.

    Втр, 02/28/2012 - 15:01 — SomNambul
    Authors: Ivan Kojadinovic, Jun Yan
    Subjects: Methodology
    Abstract

    The process comparing the empirical cumulative distribution function of the
    sample with a parametric estimate of the cumulative distribution function is
    known as the empirical process with estimated parameters and has been
    extensively employed in the literature for goodness-of-fit testing. The
    simplest way to carry out such goodness-of-fit tests, especially in a
    multivariate setting, is to use a parametric bootstrap.

  2. Large-sample tests of extreme-value dependence for multivariate copulas.

    Чт, 05/12/2011 - 15:01 — SomNambul
    Authors: Johan Segers, Ivan Kojadinovic, Jun Yan
    Subjects: Methodology
    Abstract

    Starting from the characterization of extreme-value copulas based on
    max-stability, large-sample tests of extreme-value dependence for multivariate
    copulas are studied. The two key ingredients of the proposed tests are the
    empirical copula of the data and a multiplier technique for obtaining
    approximate p-values for the derived statistics. The asymptotic validity of the
    multiplier approach is established, and the finite-sample performance of a
    large number of candidate test statistics is studied through extensive Monte
    Carlo experiments for data sets of dimension two to five.

  3. A goodness-of-fit test for bivariate extreme-value copulas.

    Пт, 02/11/2011 - 16:01 — SomNambul
    Authors: Johanna Nešlehová, Christian Genest, Ivan Kojadinovic, Jun Yan
    Subjects: Statistics
    Abstract

    It is often reasonable to assume that the dependence structure of a bivariate
    continuous distribution belongs to the class of extreme-value copulas. The
    latter are characterized by their Pickands dependence function. In this paper,
    a procedure is proposed for testing whether this function belongs to a given
    parametric family. The test is based on a Cram\'{e}r--von Mises statistic
    measuring the distance between an estimate of the parametric Pickands
    dependence function and either one of two nonparametric estimators thereof
    studied by Genest and Segers [Ann. Statist. 37 (2009) 2990--3022].

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